Hybrid Adsorption–Liquefaction Process for Postcombustion CO2 Capture

12.8 MB of data from a techno-economic optimization study comparing a hybrid CO2 capture process to standalone methods. The dataset, authored by Sunny Pawar and last updated in April 2026, explores flue gas CO2 compositions ranging from 3.5 to 30 mol %. It contains results from process models and surrogate artificial neural networks used to calculate CO2 avoided costs.

Use Cases

• Comparing CO2 avoided costs between hybrid and standalone capture processes based on the described techno-economic framework.
• Analyzing the impact of flue gas CO2 composition (3.5-30 mol %) on process performance and cost.
• Training surrogate models for process optimization based on the artificial neural network methodology described.
• Evaluating the trade-offs between CO2 purity (e.g., ≤60% vs ≥95%) and recovery levels (≥90%) for different capture technologies.

Strengths

• Data is derived from detailed process models developed in MATLAB and gPROMS Process.
• Covers a wide range of flue gas CO2 compositions from 3.5 to 30 mol %.
• Includes a detailed costing framework for techno-economic optimization.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare, author Sunny Pawar

Collection Method

Generated from system-level integrated techno-economic optimization framework using process models and surrogate ANN models.

Freshness

Last updated 2026-04-29 18:03:42; freshness should be verified.